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Record W3121755449 · doi:10.2196/25870

The Building Educators’ Skills in Adolescent Mental Health Training Program for Secondary School Educators: Protocol for a Cluster Randomized Controlled Trial

2021· article· en· W3121755449 on OpenAlexvenueno aff
Belinda Parker, Cassandra Chakouch, Mirjana Subotic-Kerry, Philip J. Batterham, Andrew Mackinnon, Jill M. Newby, Alexis E. Whitton, Janey McGoldrick, Nicole Cockayne, Bridianne O’Dea

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsMental healthRandomized controlled trialCluster randomised controlled trialMedical educationPsychologyStigma (botany)Intervention (counseling)MedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: In Australia, secondary school educators are well positioned to recognize mental illness among students and provide support. However, many report that they lack the knowledge and confidence to do so, and few mental health training programs available for educators are evidence based. To address this gap, the Black Dog Institute (BDI) developed a web-based training program (Building Educators' Skills in Adolescent Mental Health [BEAM]) that aims to improve mental health knowledge, confidence, and helping behaviors among secondary school educators in leadership positions. A pilot study of the training program found it to be positively associated with increased confidence and helping behaviors among educators and reduced personal psychological distress. An adequately powered randomized controlled trial (RCT) is needed. OBJECTIVE: The primary objective of this cluster RCT is to evaluate the effectiveness of the BEAM program for improving educators' confidence in managing student mental health. The trial will also evaluate the effect of the BEAM program in increasing educators' frequency of providing help to students and improving their mental health knowledge and reducing educators' psychological distress and stigma toward students with mental health issues. METHODS: The target sample size is 234 educators from 47 secondary schools across New South Wales, Australia. Four waves of recruitment and enrollment into the trial are planned. Schools will participate in one wave only and will be randomized to the intervention or waitlist control conditions. Participants from the same school will be assigned to the same condition. Assessments will be conducted at baseline, posttest (10 weeks after baseline), and follow-up (22 weeks after baseline) using the BDI eHealth research platform. Intervention participants will receive access to the BEAM program for 10 weeks upon completion of baseline, and the control condition will receive access for 10 weeks upon completion of the follow-up assessment. RESULTS: Recruitment for this trial began on July 21, 2020, with the first baseline assessments occurring on August 17, 2020. To date, 295 participants from 71 schools have completed baseline. Due to the unexpected success of recruitment in the first 3 waves, the final fourth wave has been abandoned. Intervention participants are currently receiving the program, with follow-up due for completion in March 2021. CONCLUSIONS: This is one of the first RCTs to examine the effectiveness of a web-based adolescent mental health training program for Australian secondary school educators in leadership positions. If found to be effective, this training program will offer a sustainable and scalable delivery method for upskilling educators in caring for students' mental health. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12620000876998; https://covid-19.cochrane.org/studies/crs-14669208. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25870.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.103
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.038
Meta-epidemiology (narrow)0.0080.005
Meta-epidemiology (broad)0.0180.007
Bibliometrics0.0040.005
Science and technology studies0.0040.004
Scholarly communication0.0060.005
Open science0.0040.002
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.1030.016

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.195
GPT teacher head0.625
Teacher spread0.430 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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